An autoencoder has the potential ability to compress and decompress information. In this work, we consider the process of generating a stego-image from an original image and watermarks as compression, and the process of recovering the original image and watermarks from the stego-image as decompression. We propose embedder and extractor neural networks based on the autoencoder. The embedder network learns mapping from the DCT coefficients of the original image and a watermark to those of the stego-image. The extractor network learns mapping from the DCT coefficients of the stego-image to the watermark. Once the proposed neural network has been trained, the network can embed and extract the watermark into unlearned test images. We investigated the relation between the number of neurons and network performance by computer simulations and found that the trained neural network could provide high-quality stego-images and watermarks with few errors. We also evaluated the robustness against JPEG compression and found that, when suitable parameters were used, the watermarks were extracted with an average BER lower than 0.01 and image quality over 35 dB when the quality factor Q was over 50. We also investigated how to represent the watermarks in the stego-image by our neural network. There are two possibilities: distributed representation and sparse representation. From the results of investigation into the output of the stego layer (3rd layer), we found that the distributed representation emerged at an early learning step and then sparse representation came out at a later step.

Side scan sonar using low frequency can quickly search a wide range, but the images acquired are of low quality. The image super resolution (SR) method can mitigate this problem. The SR method typically uses sparse coding, but accurately estimating sparse coefficients incurs substantial computational costs. To reduce processing time, we propose a region-selective sparse coding based SR system that emphasizes object regions. In particular, the region that contains interesting objects is detected for side scan sonar based underwater images so that the subsequent sparse coding based SR process can be selectively applied. Effectiveness of the proposed method is verified by the reduced processing time required for image reconstruction yet preserving the same level of visual quality as conventional methods.

It is a hot issue that speeding up the network layers and decreasing the network parameters in convolutional neural networks (CNNs). In this paper, we propose a novel method, namely, symmetric decomposition of convolution kernels (SDKs). It symmetrically separates k×k convolution kernels into (k×1 and 1×k) or (1×k and k×1) kernels. We conduct the comparison experiments of the network models designed by SDKs on MNIST and CIFAR-10 datasets. Compared with the corresponding CNNs, we obtain good recognition performance, with 1.1×-1.5× speedup and more than 30% reduction of network parameters. The experimental results indicate our method is useful and effective for CNNs in practice, in terms of speedup performance and reduction of parameters.

This paper intends to reduce duration times in typical collective communications. We introduce logical addressing system apart from the physical one and, by rearranging the logical node addresses properly, we intend to reduce communication overheads so that ideal communication is performed. One of the key issues is rearrangement of the logical addressing system. We introduce genetic algorithm (GA) as meta-heuristic solution as well as the random search strategy. Our GA-based method achieves at most 2.50 times speedup in three-traffic-pattern cases.

A parallel phrase matching (PM) engine for dictionary compression is presented. Hardware based parallel chaining hash can eliminate erroneous PM results raised by hash collision; while newly-designed storage architecture holding PM results solved the data dependency issue; Thus, the average compression speed is increased by 53%.

In real-time 3D ball tracking of sports analysis in computer vision technology, complex algorithms which assure the accuracy could be time-consuming. Particle filter based algorithm has a large potential to accelerate since the algorithm between particles has the chance to be paralleled in heterogeneous CPU-GPU platform. Still, with the target multi-view 3D ball tracking algorithm, challenges exist: 1) serial flowchart for each step in the algorithm; 2) repeated processing for multiple views' processing; 3) the low degree of parallelism in reweight and resampling steps for sequential processing. On the CPU-GPU platform, this paper proposes the double stream system flow, the view priority based threads allocation, and the binary search oriented reweight. Double stream system flow assigns tasks which there is no data dependency exists into different streams for each frame processing to achieve parallelism in system structure level. View priority based threads allocation manipulates threads in multi-view observation task. Threads number is view number multiplied by particles number, and with view priority assigning, which could help both memory accessing and computing achieving parallelism. Binary search oriented reweight reduces the time complexity by avoiding to generate cumulative distribution function and uses an unordered array to implement a binary search. The experiment is based on videos which record the final game of an official volleyball match (2014 Inter-High School Games of Men's Volleyball held in Tokyo Metropolitan Gymnasium in Aug. 2014) and the test sequences are taken by multiple-view system which is made of 4 cameras locating at the four corners of the court. The success rate achieves 99.23% which is the same as target algorithm while the time consumption has been accelerated from 75.1ms/frame in CPU environment to 3.05ms/frame in the proposed system which is 24.62 times speed up, also, it achieves 2.33 times speedup compared with basic GPU implemented work.

We propose a new context-adaptive arithmetic coding (CAAC) scheme for lossless bit rate reduction of parametric stereo (PS) in enhanced aacPlus. Based on the probability analysis of stereo parameters indexes in PS, we propose a stereo band-dependent CAAC scheme for PS. We also propose a new coding structure of the scheme which is simple but effective. The proposed scheme has normal and memory-reduced versions, which are superior to the original and conventional schemes and guarantees significant bit rate reduction of PS. The proposed scheme can be an alternative to the original PS coding scheme at low bit rate, where coding efficiency is very important.

Several types of capacitive crosstalk avoidance codes have been devised in order to prevent capacitive crosstalk in on-chip buses. These codes are designed to prohibit transition patterns prone to capacitive crosstalk from any two consecutive words transmitted to on-chip buses. The present paper provides a rigorous analysis of the asymptotic rate for (p,q)-transition free word sequences under the assumption that coding is based on a stateful encoder and a stateless decoder. Here, p and q represent k-bit transition patterns that should not appear in any two consecutive words at the same adjacent k-bit positions. The maximum rate for the sequences is proven to be equal to the subgraph domatic number of the (p,q)-transition free graph. Based on the theoretical results for the subgraph domatic partition problem, lower and upper bounds on the asymptotic rate are derived. We also show that the asymptotic rate 0.8325 is achievable for p=01 and q=10 transition free word sequences.

In this study, spatially coupled low-density parity-check (SC-LDPC) codes on the two-dimensional array erasure (2DAE) channel are devised, including a method for generating new SC-LDPC codes with a restriction on the check node constraint. A density evolution analysis confirms the improvement in the threshold of the proposed two-dimensional SC-LDPC code ensembles over the one-dimensional SC-LDPC code ensembles. We show that the BP threshold of the proposed codes can approach the corresponding maximum a posteriori (MAP) threshold of the original residual graph on the 2DAE channel. Moreover, we show that the rates of the residual graph of the two-dimensional LDPC block code ensemble are smaller than those of the one-dimensional LDPC block code ensemble. In other words, a high performance can be obtained by choosing the two-dimensional SC-LDPC codes.

Racetrack memory (RM) has attracted much attention. In RM, insertion and deletion (ID) errors occur as a result of an unstable reading process and are called position errors. In this paper, we first define a probabilistic channel model of ID errors in RM with multiple read-heads (RHs). Then, we propose a joint iterative decoding algorithm for spatially coupled low-density parity-check (SC-LDPC) codes over such a channel. We investigate the asymptotic behaviors of SC-LDPC codes under the proposed decoding algorithm using density evolution (DE). With DE, we reveal the relationship between the number of RHs and achievable information rates, along with the iterative decoding thresholds. The results show that increasing the number of RHs provides higher decoding performances, although the proposed decoding algorithm requires each codeword bit to be read only once regardless of the number of RHs. Moreover, we show the performance improvement produced by adjusting the order of the SC-LDPC codeword bits in RM.

In multilevel flash memory, in general, multiple read thresholds are required to read a single logical page. Random I/O (RIO) code, introduced by Sharon and Alrod, is a coding scheme that enables the reading of one logical page using a single read threshold. It was shown that the construction of RIO codes is equivalent to the construction of write-once memory (WOM) codes. Yaakobi and Motwani proposed a family of RIO codes, called parallel RIO (P-RIO) code, in which all logical pages are encoded in parallel. In this paper, we utilize coset coding with Hamming codes in order to construct P-RIO codes. Coset coding is a technique to construct WOM codes using linear binary codes. We leverage information on the data of all pages to encode each page. Our P-RIO codes, using which more pages can be stored than RIO codes constructed via coset coding, have parameters for which RIO codes do not exist.

Recent years have seen increasing efforts to improve the input/output performance of multilevel flash memory. In this regard, we propose a coding scheme for two-page unrestricted-rate parallel random input-output (P-RIO) code, which enables different code rates to be used for each page of multilevel memory. On the second page, the set of cell-state vectors for each message consists of two complementary vectors with length n. There are a total of 2n-1 sets that are disjoint to guarantee that they are uniquely decodable for 2n-1 messages. On the first page, the set of cell-state vectors for each message consists of all weight-u vectors with their non-zero elements restricted to the same (2u-1) positions, where the non-negative integer u is less than or equal to half of the code length. Finding cell-state vector sets such that they are disjoint on the first page is equivalent to the construction of constant-weight codes, and the number of disjoint sets is the best-known number of code words in the constant-weight codes. Our coding scheme is constructive, and the code length is arbitrary. The sum rates of our proposed codes are higher than those of previous work.

We propose an approach to allocate bandwidth for a satellite communications (SATCOM) system that includes the recent high-throughput satellite (HTS) with frequency flexibility. To efficiently operate the system, we manage the limited bandwidth resources available for SATCOM by employing a control method that allows the allocated bandwidths to exceed the communication demand of user terminals per HTS beam. To this end, we consider bandwidth allocation for SATCOM as an optimal control problem. Then, assuming that the model of communication requests is available, we propose an optimal control method by combining model predictive control and sparse optimization. The resulting control method enables the efficient use of the limited bandwidth and reduces the bandwidth loss and number of control actions for the HTS compared to a setup with conventional frequency allocation and no frequency flexibility. Furthermore, the proposed method allows to allocate bandwidth depending on various control objectives and beam priorities by tuning the corresponding weighting matrices. These findings were verified through numerical simulations by using a simple time variation model of the communication requests and predicted aircraft communication demand obtained from the analysis of actual flight tracking data.

Indoor fingerprint location based on WiFi in large-scale indoor parking lots is more and more widely employed for vehicle lookup. However, the challenge is to ensure the location functionality because of the particularity and complexities of the indoor parking lot environment. To reduce the need to deploy of reference points (RPs) and the offline sampling workload, a partition-fitting fingerprint algorithm (P-FP) is proposed. To improve the location accuracy of the target, the PS-FP algorithm, a sampling importance resampling (SIR) particle filter with threshold based on P-FP, is further proposed. Firstly, the entire indoor parking lot is partitioned and the environmental coefficients of each partitioned section are gained by using the polynomial fitting model. To improve the quality of the offline fingerprint database, an error characteristic matrix is established using the difference between the fitting values and the actual measured values. Thus, the virtual RPs are deployed and C-means clustering is utilized to reduce the amount of online computation. To decrease the fluctuation of location coordinates, the SIR particle filter with a threshold setting is adopted to optimize the location coordinates. Finally, the optimal threshold value is obtained by comparing the mean location error. Test results demonstrated that PS-FP could achieve high location accuracy with few RPs and the mean location error is only about 0.7m. The cumulative distribution function (CDF) show that, using PS-FP, 98% of location errors are within 2m. Compared with the weighted K-nearest neighbors (WKNN) algorithm, the location accuracy by PS-FP exhibit an 84% improvement.

We propose a parallel precomputation method for real-time model predictive control. The key idea is to use predicted input values produced by model predictive control to solve an optimal control problem in advance. It is well known that control systems are not suitable for multi- or many-core processors because feedback-loop control systems are inherently based on sequential operations. However, since the proposed method does not rely on conventional thread-/data-level parallelism, it can be easily applied to such control systems without changing the algorithm in applications. A practical evaluation using three real-world model predictive control system simulation programs demonstrates drastic performance improvement without degrading control quality offered by the proposed method.

Nyquist folding receiver (NYFR) is a novel reconnaissance receiving architecture and it can realize wideband receiving with small amount of equipment. As a tradeoff of non-cooperative wideband receiving, the NYFR output will add an unknown key parameter that is called Nyquist zone (NZ) index. In this letter, we concentrate on the NZ index estimation of the NYFR output. Focusing on the basic pulse radar signals, the constant frequency signal, the binary phase coded signal and the linear frequency modulation signal are considered. The matching component function is proposed to estimate the NZ indexes of the NYFR outputs without the prior information of the signal modulation type. In addition, the relations between the matching component function and the parameters of the NYFR are discussed. Simulation results demonstrate the efficacy of the proposed method.

Hazes with various properties spread widely across flat areas with depth continuities and corner areas with depth discontinuities. Removing haze from a single hazy image is difficult due to its ill-posed nature. To solve this problem, this study proposes a modified hybrid median filter that performs a median filter to preserve the edges of flat areas and a hybrid median filter to preserve depth discontinuity corners. Recovered scene radiance, which is obtained by removing hazy particles, restores image visibility using adaptive nonlinear curves for dynamic range expansion. Using comparative studies and quantitative evaluations, this study shows that the proposed method achieves similar or better results than those of other state-of-the-art methods.

In this letter, we consider the harvested-energy fairness problem in cognitive multicast systems with simultaneous wireless information and power transfer. In the cognitive multicast system, a cognitive transmitter with multi-antenna sends the same information to cognitive users in the presence of licensed users, and cognitive users can decode information and harvest energy with a power-splitting structure. The harvested-energy fairness problem is formulated and solved by using two proposed algorithms, which are based on semidefinite relaxation with majorization-minimization method, and sequential parametric convex approximation with feasible point pursuit technique, respectively. Finally, the performances of the proposed solutions and baseline schemes are verified by simulation results.

Sparse code multiple access (SCMA) based on the message passing algorithm (MPA) for multiuser detection is a competitive non-orthogonal multiple access technique for fifth-generation wireless communication networks Among the existing multiuser detection schemes for uplink (UP) SCMA systems, the serial MPA (S-MPA) scheme, where messages are updated sequentially, generally converges faster than the conventional MPA (C-MPA) scheme, where all messages are updated in a parallel manner. In this paper, the optimization of message scheduling in the S-MPA scheme is proposed. Firstly, some statistical results for the probability density function (PDF) of the received signal are obtained at various signal-to-noise ratios (SNR) by using the Monte Carlo method. Then, based on the non-orthogonal property of SCMA, the data mapping relationship between resource nodes and user nodes is comprehensively analyzed. A partial codeword transmission of S-MPA (PCTS-MPA) with threshold decision scheme of PDF is proposed and verified. Simulations show that the proposed PCTS-MPA not only reduces the complexity of MPA without changing the bit error ratio (BER), but also has a faster convergence than S-MPA, especially at high SNR values.

It is essential to improve intra prediction performance to raise the efficiency of video coding. In video coding standards such as H.265/HEVC, intra prediction is seen as an extension of directional prediction schemes, examples include refinement of directions, planar extension, filtering reference sampling, and so on. From the view point of reducing prediction error, some improvements on intra prediction for standardized schemes have been suggested. However, on the assumption that the correlation between neighboring pixels are static, these conventional methods use pre-defined predictors regardless of the image being encoded. Therefore, these conventional methods cannot reduce prediction error if the images break the assumption made in prediction design. On the other hand, adaptive predictors that change the image being encoded may offer poor coding efficiency due to the overhead of the additional information needed for adaptivity. This paper proposes an adaptive intra prediction scheme that resolves the trade-off between prediction error and adaptivity overhead. The proposed scheme is formulated as a constrained optimization problem that minimizes prediction error under sparsity constraints on the prediction coefficients. In order to solve this problem, a novel solver is introduced as an extension of LARS for multi-class support. Experiments show that the proposed scheme can reduce the amount of encoded bits by 1.21% to 3.24% on average compared to HM16.7.